Unsupervised Learning: In unsupervised learning SISR, the way of evaluation and parameter up-gradation is changing by different unsupervised learning algorithms. For example, ZSSR [1]} uses the test image and its downscaling images with the data augmentation methods to build the “training dataset" and then applies the loss function to optimize the model. In CinCGAN [2]}, a model consists of two CycleGAN [3]} is proposed, where parameters are upgraded through optimizing the generator-adversarial loss, the cycle consistency loss, the identity loss, and the total variation loss together in each cycle.
